A more fundamental approach is to redesign a JSCC scheme from scratch, directly mapping the source signal to the modulated channel input, without conversion to bits at all.
It was shown recently in [1]}, that deep neural networks (DNNs) can be used to break the complexity barrier of designing JSCC schemes for wireless image transmission.
The scheme, called DeepJSCC, showed appealing properties, such as lower end-to-end distortion for a given channel blocklength compared with state-of-the-art digital compression schemes [2]},
flexibility to adapt to different source or channel models [1]}, [2]},
ability to exploit channel feedback [5]},
and capability to produce adaptive-bandwidth transmission schemes [6]}.
Importantly, graceful degradation of image quality with respect to decreasing channel quality means that DeepJSCC is able to avoid the cliff-effect that all separation-based schemes suffer from;
which refers to the phenomenon where the image becomes un-decodable when the channel quality falls below a certain threshold resulting in unreliable transmission.
